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Fundamental and Financial Influences on the Co-movement of Oil and Gas prices

Author

Listed:
  • Derek Bunn

    (School of Business [London] - LSBU - London South Bank University)

  • Julien Chevallier

    (UP8 - Université Paris 8)

  • Yannick Le Pen

    (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Benoît Sévi

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes)

Abstract

As speculative flows into commodity futures are expected to link commodity prices more strongly to equity indices, we investigate whether this process also creates increased correlations amongst the commodities themselves. Considering U.S. oil and gas futures, we investigate whether common factors, derived from a large international data set of real and nominal macroeconomic variables by means of the large approximate factor models methodology, are able to explain both returns and whether, beyond these fundamental common factors, the residuals remain correlated. We further investigate a possible explanation for this residual correlation by using some proxies for trading intensity derived from CFTC publicly available data, showing most notably that the proxy for speculation in the oil market increases the oil-gas correlation. We thus identify the central role of financial activities in shaping the link between oil and gas returns.

Suggested Citation

  • Derek Bunn & Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas prices," Post-Print hal-01619890, HAL.
  • Handle: RePEc:hal:journl:hal-01619890
    DOI: 10.5547/01956574.38.2.dbun
    Note: View the original document on HAL open archive server: https://hal.science/hal-01619890v1
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    References listed on IDEAS

    as
    1. repec:aen:journl:2006v27-02-a04 is not listed on IDEAS
    2. Apostolos Serletis & Ricardo Rangel-Ruiz, 2007. "Testing for Common Features in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 14, pages 172-187, World Scientific Publishing Co. Pte. Ltd..
    3. James D. Hamilton, 2009. "Causes and Consequences of the Oil Shock of 2007-08," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 215-283.
    4. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    5. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    6. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    7. repec:aen:journl:2008v29-03-a03 is not listed on IDEAS
    8. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    9. Giovanni Caggiano & George Kapetanios & Vincent Labhard, 2011. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 736-752, December.
    10. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    11. repec:clg:wpaper:1999-04 is not listed on IDEAS
    12. Kallberg, Jarl & Pasquariello, Paolo, 2008. "Time-series and cross-sectional excess comovement in stock indexes," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 481-502, June.
    13. repec:aen:journl:33-2-02 is not listed on IDEAS
    14. James H. James & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," Working Papers 2005-2, Princeton University. Economics Department..
    15. Apostolos Serletis & John Herbert, 2007. "The Message in North American Energy Prices," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 13, pages 156-171, World Scientific Publishing Co. Pte. Ltd..
    16. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    17. Panagiotidis, Theodore & Rutledge, Emilie, 2007. "Oil and gas markets in the UK: Evidence from a cointegrating approach," Energy Economics, Elsevier, vol. 29(2), pages 329-347, March.
    18. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    19. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    20. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    21. Antonello D’ Agostino & Domenico Giannone, 2012. "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 306-326, April.
    22. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    23. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
    24. Bing Han, 2008. "Investor Sentiment and Option Prices," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 387-414, January.
    25. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    26. Suleyman Basak & Anna Pavlova, 2013. "Asset Prices and Institutional Investors," American Economic Review, American Economic Association, vol. 103(5), pages 1728-1758, August.
    27. Ludvigson, Sydney C. & Ng, Serena, 2007. "The empirical risk-return relation: A factor analysis approach," Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
    28. Acharya, Viral V. & Lochstoer, Lars A. & Ramadorai, Tarun, 2013. "Limits to arbitrage and hedging: Evidence from commodity markets," Journal of Financial Economics, Elsevier, vol. 109(2), pages 441-465.
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    Cited by:

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    2. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    3. Derek W. Bunn, 2021. "Observations on “Risk Transmission Across Supply Chains”," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4588-4589, December.
    4. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    5. Kim, Jong-Min & Tabacu, Lucia & Jung, Hojin, 2020. "A quantile-copula approach to dependence between financial assets," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    6. Halser, Christoph & Paraschiv, Florentina & Russo, Marianna, 2023. "Oil–gas price relationships on three continents: Disruptions and equilibria," Journal of Commodity Markets, Elsevier, vol. 31(C).
    7. Okhrin, Yarema & Uddin, Gazi Salah & Yahya, Muhammad, 2023. "Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets," Energy Economics, Elsevier, vol. 125(C).
    8. Olivier Rousse & Benoît Sévi, 2017. "Informed Trading in Oil-Futures Market," Working Papers hal-01460186, HAL.
    9. Rousse, Olivier & Sévi, Benoît, "undated". "Informed Trading in Oil-Futures Market," ESP: Energy Scenarios and Policy 249788, Fondazione Eni Enrico Mattei (FEEM).
    10. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    11. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    12. Jozef Baruník & Evžen KoÄ enda, 2019. "Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets," The Energy Journal, , vol. 40(2_suppl), pages 157-174, December.
    13. Jonathan Doh & Pawan Budhwar & Geoffrey Wood, 2021. "Long-term energy transitions and international business: Concepts, theory, methods, and a research agenda," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(5), pages 951-970, July.
    14. Guan, Qing & An, Haizhong, 2017. "The exploration on the trade preferences of cooperation partners in four energy commodities’ international trade: Crude oil, coal, natural gas and photovoltaic," Applied Energy, Elsevier, vol. 203(C), pages 154-163.
    15. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022. "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, vol. 28(C).
    16. Li, Fengyun & Li, Xingmei & Zheng, Haofeng & Yang, Fei & Dang, Ruinan, 2021. "How alternative energy competition shocks natural gas development in China: A novel time series analysis approach," Resources Policy, Elsevier, vol. 74(C).

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